Navigate What’s Next for AI in Market Research—Without the Pitfalls

The word of the moment in AI-powered market research these days? FOMO - fear of missing out. We’re all feeling it in some way. With new AI tools emerging at a rapid clip and capabilities evolving almost daily, researchers are struggling to keep up with every platform and feature. Clients tell us they are worried about getting left behind and missing the boat when it comes to supercharging their research toolkit.

We get it. From auto-generated transcription and theme coding to enhanced qualitative data exploration and accelerated processing of unstructured data—AI tools for market research are truly shaking up the industry and offering the promise of potentially game-changing breakthroughs for the way we work.

But as has been the case with every tech revolution we’ve seen in research…this all comes with a steep learning curve—and in many cases, big risks. Without a healthy dose of skepticism, the right amount of human intervention, and a proper test-and-learn mindset, major pitfalls linked with AI research tools are hard to avoid.

As a modern, agile insights agency, we help clients tackle this challenge. Acting as a "research-tech gatekeeper and advisor," we carefully vet each new tool and recommend the best use cases for our clients. In this article, we'll explore how AI is transforming market research, examine where it truly adds value, and share how we help companies like yours navigate common pitfalls while maximizing AI's potential.

How to evaluate AI tools for market research

Curious if AI tools for market research live up to the hype? Here are some key questions to ask:

• Does it reduce time to insight? (i.e. make it faster)

• Does it enable richer analysis? (i.e make it better)

• Does it save time or reduce costs? (i.e. make it cheaper)

Ideally, the tools you choose to invest time and money in should do all three. At Insight By Design, we vet new AI tools on a continuous basis and only very few pass our rigorous tests and are deemed worthy of incorporating into our own toolkit or recommending to clients. 

Every new AI tool should be met with a healthy dose of skepticism, especially at this early stage of widespread industry adoption. There are simply too many new tools being being introduced by tech players who don’t always have research expertise. And there are so many lofty promises being floated around. If you read many AI sales decks these days, you’d be forgiven if you came away thinking it all just sounds too good to be true. Many times, that’s the cold hard truth.

AI is opening up exciting possibilities in market research, but it's equally important to acknowledge where it currently falls short. The mantra should be: explore, try, verify…repeat. Better yet…find an insights partner who has already done multiple rounds of testing on a variety of tools and can share red flags, best practices and practical applications.

Strengths of AI in market research

AI brings remarkable operational advantages to market research, enhancing daily processes to make research more efficient, streamlined, and responsive. With these advancements, insights gathering can reach a level of scale and agility that were once out of reach.

But this power comes with an important caveat: skilled researchers remain indispensable. We've seen firsthand how experienced guidance is crucial to steer the process, ensure accuracy, and deliver reliable results.

Here are a few areas where we see short-term promise on the research execution side:

Rapid transcription & summaries

Before AI, transcribing interviews and reviewing survey responses took days. Now, it’s a matter of minutes. This is particularly helpful for projects that demand quick pivots, like digital diaries which capture responses over several days. AI’s rapid summaries empower researchers to refine their questions in real time based on emerging insights.

In the past, analyzing daily responses was an intense, time-consuming task. Our teams would divide respondent groups, individually comb through responses, and then reconvene to share findings. Tight timelines and massive amounts of text and video data meant crucial insights could sometimes be left to later on in analysis process vs. being able to react and follow-up in the moment while the diary research was still ongoing.

Today, AI swiftly identifies key themes from transcripts in near real time. While this is never a substitute for human-driven analysis, it does supercharge our agile research approach, allowing us to pivot on the spot and pursue interesting lines of questioning mid-project. In the end, it means even more nuanced insights that clients truly appreciate.

Distilling ideas from unstructured data

AI has fundamentally changed how researchers handle large volumes of customer feedback. The days of manually searching through thousands of comments and interview responses are over. Advanced pattern recognition now processes vast amounts of qualitative data quickly, uncovering themes and connections that might otherwise stay hidden.

AI lets us do qualitative research at a scale we never could before. In recent projects, we've analyzed detailed feedback from hundreds of participants simultaneously—far beyond what traditional focus groups could handle. The key is that we don't sacrifice depth for breadth. These tools help us spot patterns while preserving the rich, nuanced insights that make qualitative research valuable.

Where AI falls short in market research

While AI’s capabilities are impressive when it comes to boosting efficiency, we need to be clear-eyed about the serious limitations. Recent research - including a persuasive study on the limits of Large Language Models from researchers at Apple - highlights that AI lacks true reasoning skills. It simply matches patterns rather than understanding their deeper meaning. Even small changes to data, like switching names around or adding extra information, can throw its analysis off track.

The bottom line? While AI excels at identifying patterns, it requires human expertise to understand why those patterns matter. Spotting trends is one thing—understanding their strategic implications for your business is another entirely.

We see these limitations play out daily in our work. While AI helps us process more data faster, it can't replace the strategic thinking that turns findings into actionable recommendations. 

Here are some specific use cases where maximum caution must be taken when using AI:

Sentiment analysis

The plain truth is that most AI tools simply do not live up to the promise of reliable and accurate sentiment identification. While AI approaches language like a mathematical equation, human communication is layered with nuance, context, and cultural references.

For example, a customer calling a product "sick" might trigger AI to flag negative sentiment, when the comment actually expresses strong approval. These aren't just occasional one-off errors—they can lead to conclusions about customer perception and product performance that are fundamentally wrong.

This great visualization AddMaple illustrates how various AI coding tools can reach distinctively different conclusions about the sentiment behind a particular restaurant review (or series of reviews). The columns below represent a variety of different sentiment coding tools and the text of the reviews appear in the rows. The variety of colors across the rows tells you there is hardly any clear consensus across the tools when trying to decipher the sentiment behind a given review. I don’t know about you, but I don’t feel comfortable taking an ‘AI only” approach to coding a series of hundreds of open end survey responses to truly understand feedback on my client’s important logo design updates.

Survey open-end coding

We can see how AI tools for coding open-ended survey responses are intriguing. Who wouldn't want faster processing times and automated theme detection? However, despite the speed advantage, many of these tools often fail to deliver better results or cost savings. The time saved in initial coding is frequently offset by the need for extensive verification and correction.

Until significant improvements are made, we recommend spending your time and resources on other applications of AI that actually improve research efficiency and free up more storytelling (vs. wasted time fixing AI coding errors)

Synthetic data

The current push toward synthetic data generation in market research raises also serious red flags. Non-research companies, particularly new startups, are making ambitious claims about replacing real human respondents with AI-generated feedback, digital twins, and virtual audiences.

We've seen this movie before. Years ago, social listening tools promised to analyze brand sentiment across the internet at scale. Those promises never truly materialized as advertised. Today's synthetic data platforms feel similar—lots of big claims that deserve careful scrutiny.

Recent research from Kantar concludes that synthetic sample often misses crucial nuances, skews toward overly positive ratings, and creates misleading patterns in subgroup analysis. "Synthetic data has lots of potential but the industry has a lot more work to do to build technically and methodologically sound, enterprise-ready solutions," Kantar concludes.

Simply put, current AI can't replicate authentic human feedback which is unpredictable, culturally complex, and sometimes contradictory. Given these fundamental limitations, AI generated survey takers aren't ready to replace real human respondents just yet. Watch this space and we’ll alert you when we see real, measurable progress in this area.

AI tools ready for your market research toolkit…today

Instead of chasing every shiny new AI tool for market research, we only recommend tools that actually deliver tangible benefits and reliable performance. Through rigorous round of testing and real-world application, we've identified which tools truly deliver value when properly utilized by experienced researchers. The reality is that any tool’s effectiveness depends entirely on expert implementation and management. With that caveat in mind, here are just a few tools that have earned their place in our research toolkit…

Remesh

We use Remesh to combine the depth of focus groups with the scale of surveys. The platform lets us gather open-ended feedback from 75-100 participants in real time. We watch as its AI identifies common themes and runs quick voting exercises to build consensus. This gives our clients both reliable quantitative data and rich qualitative insights about what their customers really think.

Yabble

We use Yabble's chat feature to analyze interview transcripts in a new way. Instead of reviewing each transcript separately, we can explore themes across a series of interview transcripts at once - a real game changer. The AI spots patterns and pulls relevant quotes for a quick first look at trends. Our team then verifies these highlights against the original transcripts to ensure accuracy and build out more nuanced insights.

Recollective

Recollective makes our diary studies more dynamic. Its AI transcribes video responses and creates detailed summaries, eliminating hours of manual video review. This frees our team to focus on actionable insights while ensuring no critical “aha moments” slip through the cracks.

The platform's real value shines in project agility. When we spot an interesting trend in day two's responses, we adjust day three's questions to probe deeper. We can also share AI summaries with stakeholders mid-project, enabling faster decisions and more collaborative discussions. This real-time flexibility helps capture richer, more meaningful insights throughout the study.

Real-world application

How we communicate is changing globally, as people connect through devices, chat and voice.  Increasingly, younger consumers in particular prefer to interact through technology and are becoming more comfortable with AI in general.    

This has opened up the door for us as market researchers to be more innovative and creative in how we capture meaningful insights using tech and AI. One great example of this is using conversational AI in our surveys to collect rich qualitative insights at scale.  For this we partner with Human Listening (https://www.humanlistening.com/), a robust AI-driven platform that uses the latest in AI technologies to enable human-like interactions with consumers.

As participants answer open-ended questions, tailored probes using the conversational AI functionality will emerge in response to their previous answers.  Each discussion is unique because the AI-powered chatbot probes with intelligence to elicit deeper and more meaningful feedback – enabling us to collect far more detailed responses than traditional research surveys would allow.

Incorporating this functionality into our quant surveys allows us to gather these rich insights at scale.  Not 8 perspectives, as in a focus group, but 10s, 100s, or even 1,000s of unique points of view.

This approach is especially effective for complex topics and tech-savvy audiences.  Recently, one of our technology clients needed rich insights from young consumers across multiple markets quickly.  Conversational AI was a perfect way to establish and expand on our initial hypotheses and guarantee we were covering all relevant topics in the next phase of research. Our client was thrilled by the depth of insights we delivered and appreciated the time and cost savings.

Why Insight By Design is the AI guide you need

Many companies test AI platforms on their own—and we get it. The promise of quick implementation is tempting. But that trial-and-error approach can quickly become very expensive and very risky. You risk wasting budget on tools that don't deliver, making decisions based on flawed data, and losing precious time as your team climbs steep learning curves. At Insight By Design, we've already done the hard work, rigorously testing which features actually deliver and which fall short.

Our clients appreciate our practical approach. We test new AI tools in the background of real projects and through internal test cases. A tool only makes it into client work after proving its worth. This protects you from wasted time and resources while keeping our capabilities current.

Expert analysis remains essential in research. AI excels at organizing and distilling information quickly, but it takes skilled analysts to make sense of it all. Our team connect the dots that AI can't, providing context and critical thinking to draw conclusions that matter for your business. 

We help clients maximize AI's potential while avoiding common pitfalls—like AI hallucination where tools make up plausible-sounding information. Our deep expertise in both research and AI technology means you get the best of both worlds: cutting-edge capabilities guided by sound research principles.

What's next for AI in market research

AI capabilities continue to expand in market research, and we're watching two areas closely. Visual analysis stands out as a significant opportunity. While most of our qualitative research currently relies on text analysis from transcripts and survey responses, we're seeing more clients collect rich visual data—photos and videos of homes, shopping trips, and product use. As AI's ability to analyze images at scale improves, we'll unlock deeper insights into how people really live and shop.

We're also tracking developments in adaptive generative AI for concept testing. Imagine testing new concepts in real time based on participant feedback. If survey respondents suggest an ad would resonate better with a diverse family, AI could quickly generate that version for immediate testing. This means faster iterations and more dynamic research. Maybe you’ll see this in the not-too-distant future…perhaps in a project partnering with us!

A balanced approach to AI in market research

AI is brining valuable improvements to our work as researchers — faster transcription, better pattern identification, and scaled qualitative research, to name a few. But its limitations around areas like sentiment analysis, synthetic data, and complex reasoning show why human expertise remains absolutely essential.

Success in modern research requires combining AI's capabilities with human insight. This balanced approach delivers accurate, relevant, and meaningful insights while avoiding costly missteps. At Insight By Design, we've already navigated the complexities of AI adoption and testing. We prioritize human connection in all aspects of what we do and we won’t sacrifice that commitment just because AI is shaking things up! 

Let us help you find the right mix of innovation and expertise for your research needs. Contact us to start your AI-powered research journey with a truly experienced team at your side.